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Data Analytics

Effective maintenance of rolling stock thanks to big data

Many delays in rail traffic are caused by rolling stock malfunctions. On rail cars, so-called "door defects" are frequently registered - but analysis reveals that such reports are due to a number of very different reasons. In close cooperation with SBB, Zühlke developed a data lab to analyze entire sets of large and complex data on door and other system faults in order to take appropriate action. Thanks to the data lab, the time required for the analysis of big data was reduced by more than 98%. This project laid the foundation for the use of big data analytics at SBB Passenger Operating.

Open doors as obstacles

"The S12 is running late due to a malfunction" - announcements like this are triggered by system faults in the rolling stock, very often involving door failures. A tiny gap of just a few millimeters causes the monitor to report an open door - and the entire train is immobilized. On account of the tight schedules, such system disorders can easily result in delays nationwide as trains in Switzerland call at 794 stations and stop in hourly or half-hourly intervals - in urban areas even more frequently.

Considering these figures, the SBB is very keen to analyze system faults and to identify the real causes, like operational errors, defective control technology, pneumatics etc., and to prevent future disruption with targeted measures.

Customer Statement

The data lab provides us with an overall view of our data - this makes our analytical work more efficient and effective.

A data lab for all eventualities

Which train systems are particularly prone to outages? Are there routes or stations where faults are particularly common? Which train types are most likely to have door malfunctions? Answers to questions of this nature are very significant for SBB. In the past, the analysis of the causes was extremely complicated. Large amounts of data - for example on failures, delays, operational services, maintenance tasks - had to be summarized manually from various sources, synchronized, linked logically and validated in order to even start with the analysis.

To simplify the process, a data lab was required that would provide quick access to all the information and a user-friendly analysis. Thanks to extensive cross-industry expertise and experience in big data projects, Zühlke was eminently qualified for the task. The team, consisting of data analysts, data platform specialists and a project manager, developed a data lab and a dashboard.

"Self-service" big data analytics

Zühlke's data analysts conducted complex data analysis on specific issues. They developed the required data lab in an agile process in close cooperation with the responsible department at SBB. The lab reduced the time for a fault analysis from roughly 5 days to just half an hour. Thanks to an incorporated analysis tool and practical training in its usage by Zühlke's data analysts, the maintenance specialists can now perform the fault analyses themselves – while devoting all their energies to the content.

The project had a pioneering character for SBB: It could be the beginning of a new decision-making culture based on big data. The new data lab is currently used for diagnostics, but it also provides the basis for future further applications, e.g. for predictive maintenance.

Customer benefits at a glance

Extensive savings and well-equipped for the future:

Massive time savings: The administration of all the relevant data in a pool considerably shortens the time required for fault analysis.

Substantial cost reductions: Reducing system downtimes, for instance through targeted maintenance measures, translates to a significant optimization of railway operations with corresponding savings.